COVID-19与动脉粥样硬化不稳定斑块共同疾病基因的筛选及鉴定  

Screening and Identification of Common Disease Genes of COVID-19 and Atherosclerotic Unstable Plaques

作　　者：尤红俊 苟棋玲 董梦雅 常凤军[1] YOU Hongjun;GOU Qiling;DONG Mengya;CHANG Fengjun(Department of Cardiovascular Medicine,Shaanxi Provincial People's Hospital,Xi'an 710068,China)

机构地区：[1]陕西省人民医院心血管内科,陕西省西安市710068

出　　处：《实用心脑肺血管病杂志》2024年第10期82-87,93,共7页Practical Journal of Cardiac Cerebral Pneumal and Vascular Disease

基　　金：陕西省自然科学基础研究计划项目(2024JC-YBQN-0936);陕西省人民医院2023科技发展孵化基金项目(2023YJY-63)。

摘　　要：目的筛选并鉴定COVID-19与动脉粥样硬化(AS)不稳定斑块的共同疾病基因。方法从基因表达综合(GEO)数据库下载GSE164805、GSE28829、GSE41571、GSE163154数据集,然后将GSE28829、GSE41571数据集作为AS训练集,GSE163154数据集作为AS验证集。筛选GSE164805数据集中COVID-19和健康对照者(HC)间的差异表达基因(DEGs)及AS训练集中AS不稳定斑块和AS稳定斑块间的DEGs。绘制COVID-19和HC间的DEGs与AS不稳定斑块和AS稳定斑块间的DEGs的韦恩图,筛选COVID-19与AS不稳定斑块的共同DEGs。将共同DEGs导入String数据库进行蛋白质-蛋白质互作(PPI)网络分析,以最大相关准则(MCC)值排序前10位的基因作为共同核心DEGs。在GSE164805数据集和AS训练集中分析10个共同核心DEGs的表达水平。绘制ROC曲线以分析10个共同核心DEGs对AS验证集AS不稳定斑块的预测效能。结果在GSE164805数据集中,COVID-19和HC间的DEGs共5379个。在AS训练集中,AS不稳定斑块和AS稳定斑块间的DEGs共1615个。韦恩图分析结果显示,COVID-19与AS不稳定斑块的共同DEGs共210个。PPI网络分析结果显示,取MCC排序前10位的基因作为共同核心DEGs,分别为TLR2、FCGR3B、TLR8、CSF1R、MMP9、TLR1、CD163、SPI1、CD80、C5AR1。在GSE164805数据集中,COVID-19的TLR2、FCGR3B、TLR8、CSF1R、MMP9、TLR1、CD163、SPI1、CD80、C5AR1表达水平高于HC(P<0.05);在AS训练集中,AS不稳定斑块的TLR2、FCGR3B、TLR8、CSF1R、MMP9、TLR1、CD163、SPI1、CD80、C5AR1表达水平高于AS稳定斑块(P<0.05)。ROC曲线分析结果显示,TLR2、FCGR3B、TLR8、CSF1R、MMP9、TLR1、CD163、SPI1、CD80、C5AR1预测AS验证集AS不稳定斑块的AUC分别为0.873〔95%CI(0.746~0.999)〕、0.565〔95%CI(0.385~0.746)〕、0.843〔95%CI(0.691~0.994)〕、0.953〔95%CI(0.898~1.000)〕、0.938〔95%CI(0.861~1.000)〕、0.831〔95%CI(0.694~0.968)〕、0.951〔95%CI(0.895~1.000)〕、0.981〔95%CI(0.931~1.000)〕、0.935〔95%CI(0.856~1.000)〕、0.984〔95%CI(0.958~1Objective To screen and identify the common disease genes of COVID-19 and atherosclerotic(AS)unstable plaques.Methods The GSE164805,GSE28829,GSE41571,GSE163154 data sets were downloaded from the Gene Expression Omnibus(GEO)database,and then the GSE28829 and GSE41571 data sets were used as the AS training set,and the GSE163154 data set was used as the AS validation set.Differentially expressed genes(DEGs)between COVID-19 and healthy controls(HC)in GSE164805 data set and DEGs between AS unstable plaques and AS stable plaques in AS training set were screened.Venn diagram of DEGs between COVID-19 and HC and DEGs between AS unstable plaque and AS stable plaque was drawn,and the common DEGs of COVID-19 and AS unstable plaques were screened.The common DEGs were imported into the String database for protein-protein interaction(PPI)network analysis,and the top 10 genes ranked by maximum correlation criterion(MCC)value were used as common core DEGs.The expression levels of 10 common core DEGs were analyzed in the GSE164805 data set and the AS training set.ROC curve was drawn to analyze the predictive efficacy of 10 common core DEGs for AS unstable plaques in AS validation set.Results In the GSE164805 dataset,there were 5379 DEGs between COVID-19 and HC.In the AS training set,there were 1615 DEGs between AS unstable plaques and AS stable plaques.The results of Venn diagram analysis showed that there were 210 common DEGs of COVID-19 and AS unstable plaque.The results of PPI network analysis showed that the top 10 genes ranked by MCC were selected as common core DEGs,which were TLR2,FCGR3B,TLR8,CSF1R,MMP9,TLR1,CD163,SPI1,CD80 and C5AR1,respectively.In the GSE164805 dataset,the expression levels of TLR2,FCGR3B,TLR8,CSF1R,MMP9,TLR1,CD163,SPI1,CD80 and C5AR1 in COVID-19 were higher than those in HC(P<0.05).In the AS training set,the expression levels of TLR2,FCGR3B,TLR8,CSF1R,MMP9,TLR1,CD163,SPI1,CD80 and C5AR1 in AS unstable plaques were higher than those in AS stable plaques(P<0.05).The results of ROC curve analysis showed tha

关 键 词：新型冠状病毒感染 动脉粥样硬化 粥样斑块 基因 

分 类 号：R563.12[医药卫生—呼吸系统] R543.2[医药卫生—内科学]